Capacitated location routing problem with simultaneous pickup and delivery under the risk of disruption

Abstract

This paper develops a new mathematical model to study a location-routing problem with simultaneous pickup and delivery under the risk of disruption. A remarkable number of previous studies have assumed that network components (e.g., routes, production factories, depots, etc.) are always available and can permanently serve the customers. This assumption is no longer valid when the network faces disruptions such as flood, earthquake, tsunami, terrorist attacks and workers strike. In case of any disruption in the network, tremendous cost is imposed on the stockholders. Incorporating disruption in the design phase of the network will alleviate the impact of these disasters and let the network resist disruption. In this study, a mixed integer programming (MIP) model is proposed that formulates a reliable capacitated location-routing problem with simultaneous pickup and delivery (RCLRP-SPD) services in supply chain distribution network. The objective function attempts to minimize the sum of location cost of depots, routing cost of vehicles and cost of unfulfilled demand of customers. Since the model is NP-Hard, three meta-heuristics are tailored for large-sized instances and the results show the outperformance of hybrid algorithms comparing to classic genetic algorithm. Finally, the obtained results are discussed and the paper is concluded.

Optimizing a bi-objective location-allocation-inventory problem in a dual-channel supply chain network with stochastic demands

“…Dehghani et al (2018) studied the problem of location-inventory by combining mathematical modeling and Markov process technique in a sustainable supply chain. Dehghan et al (2021) studied the location-routing problem with the aim of minimizing location and distribution costs under the risk of disruption. Amiri-Aref et al (2018) optimized multi-source location and inventory management decisions under stochastic demand and inventory policy (s, S) by formulating a two-stage profit maximization model.…”

Logistics Distribution Location Algorithm Based on Improved Imperial Competition Algorithm

“…Constraint ( 14) represents the capacity constraint of the vehicle. Constraint (15) indicates the capacity constraint of the distribution center. Formula ( 16) represents the balance constraint of flow at both ends of the customer point.…”

“…Among them, f(U, V, W) is the objective function expression (4), and the path reliability constraint (5), vehicle capacity constraint (14), and distribution center capacity constraint (15) are added to the evaluation function as penalty items. ϵ 1 , ϵ 2 , and ϵ 3 are penalty coefficients.…”

“…ey put forward a mixed-integer programming model. In the supply chain distribution network, the model formulated the reliable capacity positioning path problem with synchronous delivery and service delivery [15]. However, so far, the LRP problem considering the reliability of CO 2 emission has been less studied.…”

Bi‐objective optimization of a stochastic resilient vaccine distribution network in the context of the COVID‐19 pandemic

“…Two significant challenges in front of designing any distribution network are the uncertainty of the input parameters [16] , [17] and the disruption of different network components [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] . Accordingly, in finding optimal vaccination strategies and designing an optimal distribution network, the uncertainty of input parameters (e.g., the effectiveness of different vaccines, the progress of the pandemic, the emergence of new variants of the virus, etc.)…”